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MINT Technical Overview
October 8th, 2010
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Agenda
• MINT Goals
• DICOM Challenges, MINT Solutions
• MINT Realized
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MINT Goals
• Improve Transfer Speed for DICOM
Studies
• Eliminate need for DICOM routing /
application specific caches
• Centralize QC Logic
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Typical Enterprise Architecture Today
Enterprise Viewer
Proprietary
Enterprise Viewer
Server
DICOM
VNA
PACS
Workstation
Acquisition Device
DICOM
DICOM
DICOM Router
Proprietary
PACS
Archive
DICOM
DICOM
Advanced Visualization
Workstation
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Typical Viewer and Archive Architectures Today
Viewer (2D, 3D, etc)
Archive (PACS, VNA, etc)
DICOM I/O
Data
Validation/
QC/Admin
Processing /
Clinical
Validation
Viewer
Storage
DICOM P10
Calculated Data
DICOM I/O
HL7
Web
Data Validation/QC/Admin
Storage
DICOM P10
Note: Many components are duplicated which creates complexity and cost
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DICOM Encoding
DICOM has a normalized
data model:
Study
Series
SOP Instance
Pixel Data
Study
Series
SOP Instance
Pixel Data
But transmission is organized at the
SOP Instance level:
Study
Series
SOP Instance
Pixel Data
SOP Instance
Pixel Data
Challenge #1:
When an instance is transmitted, the study and series level information
Is denormalized (replicated) in each SOP Instance. This causes validation problems
Challenge #2:
There is no way to access the metadata (non pixel data) without pulling the pixel
Data. The pixel data is 99% of the size of the study, yet not all pixel data is always
required. The viewer usually needs access to all metadata to understand the study.
Challenge #3:
DICOM uses a custom encoder that requires a special library to understand.
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DICOM Transport
PACS
Viewer
Get Image #1
Several minutes
over Gig/E
Return Image #1
Get Image #2
Return Image #2
….
Get Image #2000
Return Image #2000
Once all images have been received, the viewer can figure out how to display them
Challenge #4:
DICOM is a chatty protocol which prevents it from
fully utilizing the bandwidth available in high speed
networks
Challenge #5:
The DICOM protocol is specific to medical imaging and
does not benefit from the ongoing technology advances
found in standard protocols like HTTP
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MINT Encoding
Study
Series
SOP Instance
SOP Instance
Pixel Data
Pixel Data
Benefit #1:
MINT matches the DICOM logical model which is normalized – no data is duplicated
Benefit #2:
MINT allows access to the metadata independently from the pixel data
Benefit #3:
MINT encodes the metadata in XML – the enterprise standard for encoding
information
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MINT Transport
PACS
Viewer
Several seconds
over Gig/E
Get Metadata
Return metadata
Once metadata is received, the viewer can figure out what images it needs
Get Image #1-2000
Return Images #1 - 2000
Benefit #4:
MINT can fully leverage high speed networks by
supporting batch requests. Individual images can also be
requested (batch of size 1) to support streaming or on
demand use cases
Benefit #5:
MINT leverages HTTP – the enterprise standard
technology for information transport. This allows it to
benefit from related technology advances and general
understanding by IT
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Other MINT Features
• Basic search capabilities
– Client can search on specific keys
– Not intended to directly support workflow
• Changelog mechanism
– Client can determine what has changed on a MINT server (new
studies, images added, etc)
• Support for proprietary data
– Client can store proprietary data (e.g. volumes, snapshots, etc)
• Data Dictionary
– Stores the schema for each study type including normalization
rules
• Support for modifying studies
– Add, update, delete normalized entities
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MINT Realized
• Improve Transfer Speed for DICOM Studies
– Leverage HTTP
– Separate metadata from pixel data
– Batch and streaming mechanisms for pixel data
• Eliminate need for DICOM routing / application specific caches
– Storage of non DICOM (proprietary) objects at the study level
– Changelog simplifies synchronization
– Query mechanism supports common on demand loading strategies
• Centralize QC Logic
– Normalized entity modification mechanism simplifies this
– Elimination of data caches results in updates only needing to take place
in one location
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MINT Study Types
MINT Study
Metadata
Metadata
Binary Data
Binary Data
DICOM
Vendor B Proprietary
Metadata
Metadata
Binary Data
Binary Data
Vendor A Proprietary
AIM
MINT is based on an extendable type system that is study oriented. One of the
standardized MINT types is DICOM which specifies a mapping from DICOM to MINT
encoding. Proprietary types can be added to store application specific data such as
vendor specific 3D Volumes, XML documents, AIM documents, etc.
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Logical View of the DICOM MINT Type
•Metadata
• Includes all non binary attributes found in all DICOM SOP
instances in a given study
•Normalized attributes according to DICOM Information Model
• Is accessed independently from the binary data
• References binary data items using their id
•Binary Items
• Can be retrieved in batch or individually
• Are identified by study scoped ids (bids or binary ids)
Voice Clip
Private Attributes
x
Patient
Image
Study
Series
Series
Series
SOP Instances
SOP Instances
SOP Instances
Metadata
Binary Data
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A Study’s History
Time
5 min
1 Hour
2 Days
P10
Instance 1
P10
Instance 2
P10
Instance 3
P10
Instance 4
P10
Instance 5
P10
Instance 6
P10
Instance 7
P10
Instance 8
P10
Instance 9
P10
Instance 10
P10
Instance 11
P10
Instance 12
P10
Instance 13
P10
Instance 14
Instance Sequence 1
Instance Sequence 2
Metadata Item
Instance Sequence 3
Series 1, 2 & 3
(From DICOM GW)
Series 4
(From DICOM GW)
Patient Name Change
(From Client)
Presentation State Added
(From Client)
•
•
•
•
Patient
Studies are not created at one single point in time
New SOP Instances may be added to a study at any time
Changes to existing instances may be made at any time
Applications may add proprietary data to a study at any time
P10
Instance 15
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MINT Based Enterprise Architecture
Acquisition Device
MINT
MINT
Enterprise Viewer
MINT
Archive
PACS
Workstation
Proprietary
MINT
PACS
Server
Proprietary
MINT
Enterprise
Viewer
Server
MINT
Advanced
Visualization
Workstation
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MINT Impact on both Architectures
Viewer (2D, 3D, etc)
Archive (PACS, VNA, etc)
DICOM I/O
HL7
Web
Admin
Viewer
Data Validation/QC/Admin
Processing /
Clinical
Validation
HTTP
HTTP
HTTP
MINT
Storage
DICOM P10
Calculated Data